Data Visualization

Hello!

I am Ayush.
I work at the intersection of data, policy and development.
I sometimes teach data analysis skills using R to those who will suffer me.

What is the talk about?

I think the two must read books on data viz are Data Visualization by Kieran Healy and Fundamentals of Data Visualization by Claus Wilke.

I will walk through some interesting examples from these books.
We will look at motivations for data visualization
Markers of good and bad data visualization
Some common mistakes to avoid
Where and how to learn more

Can you tell me something?

Following is the number of time I day dream about food, recorded over two weeks:

 [1] 29 16 24 19 29 25 21 25 25 25 22 23 25 20

Here is the number of times I tried to complete this presentation over the same period of time:

 [1] 17 15 15 15 21 20 26 19 36 28 22 21 26 21

It is not easy and intuitive to look at number and say something about it. Even if summary stats are provided. Let us look at this more closely.

You all know of the quartet - Anscombe and friends

   x1 x2 x3 x4    y1   y2    y3    y4
1  10 10 10  8  8.04 9.14  7.46  6.58
2   8  8  8  8  6.95 8.14  6.77  5.76
3  13 13 13  8  7.58 8.74 12.74  7.71
4   9  9  9  8  8.81 8.77  7.11  8.84
5  11 11 11  8  8.33 9.26  7.81  8.47
6  14 14 14  8  9.96 8.10  8.84  7.04
7   6  6  6  8  7.24 6.13  6.08  5.25
8   4  4  4 19  4.26 3.10  5.39 12.50
9  12 12 12  8 10.84 9.13  8.15  5.56
10  7  7  7  8  4.82 7.26  6.42  7.91
11  5  5  5  8  5.68 4.74  5.73  6.89

# A tibble: 4 × 3
  variable  mean    sd
  <chr>    <dbl> <dbl>
1 y1        7.50  2.03
2 y2        7.50  2.03
3 y3        7.5   2.03
4 y4        7.50  2.03

Corr x1, y1: 0.8164205
Corr x1, y1: 0.8162365
Corr x1, y1: 0.8162867
Corr x1, y1: 0.8165214

Things are not always as they seem

Anscombe’s quartet-from Data Visualization by Healy

Clearly, looking at data helps

Helps the build intuitive understanding of the data
Identify patterns, sometimes expected, sometimes unexpected
Convey a lot of information in a concise an accessible and memorable manner
All the points are true for the people generating as well as consuming a visualization

Can we identify an effective vs a bad visualization

Napolean’s retreat from Russia by Minard-from Data Visualization by Healy

`Monstrous Costs’ by Nigel Holmes-from Data Visualization by Healy

Can we identify an effective vs a bad visualization

Rainfall in Glasgow and Edinbrugh-from Cara Thompson’s More than pretty graphs

Tufte on Visualization

“Graphical excellence is the well-designed presentation of interesting data—a matter of substance, of statistics, and of design … [It] consists of complex ideas communicated with clarity, precision, and efficiency. … [It] is that which gives to the viewer the greatest number of ideas in the shortest time with the least ink in the smallest space … [It] is nearly always multivariate … And graphical excellence requires telling the truth about the data. (Tufte, 1983, p. 51).”

Markers of a good visualization

Not just about how it looks, though this makes the graph memorable
Depends who is looking at it
Why are thy looking at it - what is expected out of the chart
Essentially, you need both, good taste as well as an understanding of how human visual perception works. The latter can be learned with practice and in relatively less time than the first. Good taste needs to be developed and it takes time.

A discussion on this figure

reference to NYT graph-from Data Visualization by Healy

Identifying features of bad visualizations

The following problems are distinct but can appear in in various combinations in a given figure.

Strictly Aesthetic
Substantive - the data presented is somehow off
Perceptual

Back to Democracy - what are the good things?

What if I tell you

These are are not the responses from a longitudinal survey
Actually, it is the same question asked to people born in different decades, i.e., different age groups.
Cherry on top it was not a binary question
Identifying substantive problems require understanding of underlying data of a chart, being observant of any transformations and consequent effects, etc..

A good samaritan

Voeten’s response to NYT graph-from Data Visualization by Healy

One for the finance bros

Liz Ann Sonders, Chief Investment Strategist with Charles Schwab, Inc,-from Data Visualization by Healy

What if it was shown this way

Healy’s examples for possible manipulations from Data Visualization by Healy

How to address such issues - Healy’s Alternative

Healy’s Alternative to the index vs money base chart from Data Visualization by Healy